March 2020
Intermediate to advanced
384 pages
13h 10m
English
Content preview from Deep Reinforcement Learning in ActionBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 4. Learning to pick the best policy: Policy gradient methods
This chapter covers
- Implementing the policy function as a neural network
- Introducing the OpenAI Gym API
- Applying the REINFORCE algorithm on the OpenAI CartPole problem
In the previous chapter we discussed deep Q-networks, an off-policy algorithm that approximates the Q function with a neural network. The output of the Q-network was Q values corresponding to each action for a given state (figure 4.1); recall that the Q value is the expected (weighted average) of rewards.
Figure 4.1. A Q-network takes a state and returns Q values (action values) for each action. We can use those action values to decide which actions to take.
Given these predicted Q values from the Q-network, ...
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